Allow collaborative filtering evaluators to use custom logic in splitting data
set
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Key: MAHOUT-906
URL: https://issues.apache.org/jira/browse/MAHOUT-906
Project: Mahout
Issue Type: Improvement
Components: Collaborative Filtering
Affects Versions: 0.5
Reporter: Anatoliy Kats
Assignee: Sean Owen
Priority: Minor
Fix For: 0.6
I want to start a discussion about factoring out the logic used in splitting
the data set into training and testing. Here is how things stand: There are
two independent evaluator based classes:
AbstractDifferenceRecommenderEvaluator, splits all the preferences randomly
into a training and testing set. GenericRecommenderIRStatsEvaluator takes one
user at a time, removes their top AT preferences, and counts how many of them
the system recommends back.
I have two use cases that both deal with temporal dynamics. In one case, there
may be expired items that can be used for building a training model, but not a
test model. In the other, I may want to simulate the behavior of a real system
by building a preference matrix on days 1-k, and testing on the ratings the
user generated on the day k+1. In this case, it's not items, but
preferences(user, item, rating triplets) which may belong only to the training
set. Before we discuss appropriate design, are there any other use cases we
need to keep in mind?
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